Introduction to R

[NAME]

Why is R programming a useful skill?

(John Drake)

About Myself

  • My research interest is [INSERT DESCRIPTION]
  • I use R in my research to:
    • organize data
    • develop machine learning algorithms
    • [INSERT DETAILS]
  • I mostly work [in this disease system or approach]
  • My contact info is [INSERT EMAIL]

Workshop Goals

The workshop series consists of four half day workshops each focusing on a different part of the research process

  • Introduction to R (Today)
  • Reproducible research (May 30; Robbie Richards)
  • Simulating infectious diseases (June4; John Vinson)
  • Visualizing data (June 6; Michelle Evans)

workflow

Workshop Format

  • Introduction of instructor & how they use R
  • Review of past concepts
  • Outline of the day's goals
  • Work through data analysis project
  • Reflection on day's materials

Expectations

You

  • Come ready to participate
    • review the materials before hand
    • room tends to be cold
  • Ask questions
  • Help each other

Instructor

  • Come ready to participate
  • Help you find answers
  • Be a resource for you during the REU program

Outline

At the end of this workshop you should be able to…

  • calculate summary statistics of a dataset
  • create a figure from data

Topics

  1. What is R and RStudio?

  2. Introduction to with tidyverse with code along

    Break

  3. Troubleshooting

  4. Exploring data in groups

  5. Wrap Up

1. What is R and RStudio?

R logo

  • R is a free, open source programming language with a pre-set functionality (base R)
  • Functionality is extented by R packages which are collections of functions and data sets developed by the community.

RStudio logo

  • RStudio is a software program that makes R programming easier by:
    • write and test code efficiently
    • orginizing files into projects
    • intergrating different programming lanuguages (RMarkdown, Latex, etc.)

RStudio

Let's open today's exercise by clicking on W1_Exercise.Rproj in the W1_Exercise folder.

RStudio should automatically open from here.

RStudio

RStudio

2. Introduction to tidyverse

  • The tidyverse is a set of packages that work in harmony because they share common design.
  • Unique syntax used pipes (%>%) to connect data (object) to verbs (functions).

tidy syntax

tidyverse package

The tidyverse package is designed to make it easy to install and load core packages from the tidyverse in a single command.

tidy verse package

Install tidyverse package

  • We need to install the tidyverse package. This can be done by clicking on buttons in Rstudio or from the console using install.packages()
  • Install the package by running line 21 of the W1_Exercise.Rmd file
# install all the packages in the tidyverse
install.packages("tidyverse")

Load tidyverse package

  • Once a package is installed, we need to load it during our current R session.
  • This is done using library().
  • How should this code be altered to load the tidyverse library? 
# load tidyverse library
library()

Switch over to W1_Exercise.Rmd

Break

3. How to get unstuck: functions

  • Every function has a help page
  • ?function() to access

manual page

How to get unstuck: packages

  • Packages have a vignette and/or reference manual on cran.r-project.org

  • People often make their own tutorials too

How to get unstuck: packages

  • Cheatsheets published out by RStudio

cheatsheets

https://www.rstudio.com/resources/cheatsheets/

How to get unstuck: packages

  • Cheatsheets published out by RStudio
  • Contributed cheatsheets

cheatsheets

How to get unstuck: error messages

error

First, try to understand the error message. It can be very helpful.

How to get unstuck: error messages

  • But sometimes, the messages is cryptic. So use google!

google

4. Exploring further

Let's work in small groups (max [X])

  • Develop a question about that can be answered with this data
  • Decide on the needed verbs, and order
  • Write the code in the final code chunck of the W1_Exercise.Rmd file

More instructions in the file.

5. Wrap Up

We can…

  • calculate summary statistics of a dataset
  • create a figure from data

using

  • dplyr package
    • filter, arrange…
  • ggplot2 package
    • geom_point, facet…

W1

5. Wrap Up

Next

Focus on communication by following best practices for reproducible research

W1